Publications

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2019
Devasia, JV, Chandran P.  2019.  Graph pruning based approach for inferring disease causing genes and associated pathways. International Journal of Bioinformatics Research and Applications. 15:359–370., Number 4: Inderscience Publishers (IEL) Abstract
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Devasia, JV, Chandran P, Soman A, Mathew AE, Jharwal J.  2019.  Graph sparsification with parallelization to optimize the identification of causal genes and dysregulated pathways. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. :747–753. Abstract
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2018
Devasia, JV, Chandran P.  2018.  Inferring disease causing genes and their pathways: GpRr method. Journal of computational science. 26:108–117.: Elsevier Abstract
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2017
Devasia, JV, Chandran P, Shreya G, R A, R A.  2017.  On parallelizing graph theoretical approaches for identifying causal genes and pathways from very large biological networks. Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing. :1–6. Abstract
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2016
Devasia, JV, Chandran P.  2016.  "Who are the key players behind a disease state": Outcomes of a new computational approach on cancer data, March. 2016 International Conference on Bioinformatics and Systems Biology (BSB). :1-4. Abstract

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Devasia, JV, Chandran P.  2016.  Inferring disease causing genes and their pathways: A mathematical perspective. arXiv preprint arXiv:1611.02538. Abstract
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Devasia, JV, Chandran P.  2016.  “Who are the key players behind a disease state?”: Outcomes of a new computational approach on cancer data 2016 International Conference on Bioinformatics and Systems Biology (BSB). :1–4.: IEEE Abstract
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2014
Devasia, J, Chandran P.  2014.  Towards an Improved Algorithm for Modeling Information Flow in Biological Networks, 2014. ACCIS 2014. , Kollam, Kerala, India Abstract
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Paper - 10's Abstract
Modeling communications between nodes in large-scale molecular interaction networks as information flows is useful for analyzing relationships between individual network components. Discovering causal genes and dysregulated pathways using network analysis based on information flow models is a very active, current topic of research. Recent research provides cubic order polynomial time solutions to the problem. Considering the huge size of interaction networks, calculating information flows can be costly, even with cubic order algorithms. An improvement to the computing time has been achieved by using the concept of approximation algorithms which are useful in solving large instances of problems requiring numerous resources. Proofs for the approximation factor and implementation results for the proposed algorithm are presented.

Devasia, JV, Chandran P.  2014.  Towards an improved algorithm for modeling information flow in biological networks. International Conference on Advances in Computing, Communications, and Information Science. :88–95. Abstract
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